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Generalization bounds of incremental SVM.

Authors :
Zeng, Jingjing
Zou, Bin
Qin, Yimo
Xu, Jie
Source :
International Journal of Wavelets, Multiresolution & Information Processing. Nov2024, Vol. 22 Issue 6, p1-34. 34p.
Publication Year :
2024

Abstract

Incremental learning is one of the effective methods of learning from the accumulated training samples and the large-scale dataset. The main advantages of incremental learning consist of making full use of historical information, reducing the training scale greatly and saving space and time consumption. Despite extensive research on incremental support vector machine (SVM) learning algorithms, most of them are based on independent and identically distributed samples (i.i.d.). Not only that, there has been no theoretical analysis of incremental SVM learning algorithms. In this paper, we mainly study the generalization bounds of this incremental SVM learning algorithm whose samples are based on uniformly geometric Markov chains, and exponentially strongly mixing sequence. As a special case, we also obtain the generalization bounds of i.i.d. samples. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02196913
Volume :
22
Issue :
6
Database :
Academic Search Index
Journal :
International Journal of Wavelets, Multiresolution & Information Processing
Publication Type :
Academic Journal
Accession number :
181623531
Full Text :
https://doi.org/10.1142/S0219691324500279